Maximization of productivity is an increasingly important goal in industrial operations. This goal can be accomplished in part by various forms of quality control. Quality control is affected by numerous factors, including wear and tear of machine tool components which can lead to poor quality product. The efficiency of cutting tools in particular, such as those in milling or drilling operations, can be substantially increased through the use of systems and methods for detecting the failure of the cutting tool. Such systems and methods recognize failure of the cutting tool and allow for its replacement to prevent production of product that falls below minimum quality standards. In addition to quality improvement, such systems and methods improve efficiency by decreasing machine tool down-time and reducing overall tooling costs.
Various systems and methods exist for analysis of rotating machines in general. Two papers authored by S.G. Braun and B.B. Seth entitled "On The Extraction And Filtering Of Signals Acquired From Rotating Machines" published in the JOURNAL OF SOUND AND VIBRATION in 1979, and "Signature Analysis Methods And Applications For Rotating Machines", published in AMERICAN SOCIETY OF MECHANICAL ENGINEERS in 1977, disclose diagnostic analysis of internal combustion engines. The periodic nature of the signal acquired from a rotating machine, such as an internal combustion engine, can be correlated to given states of the machine. Signals are analyzed through both time and frequency domain signal processing techniques including filtering, digitizing and Fourier transformations.
Similar signal analysis based approaches have been employed for investigating the wear or lifespan of drills in industrial drilling operations. Once again, both time and frequency domain analysis of the vibration signal generated by the drilling process, including techniques such as filtering, digitizing and Fourier transformation, can be employed to analyze the state of the drill. In "Vibration-Based Drill Wear Monitoring", authored by J. Rotberg, E. Lenz and S. Braun, published in AMERICAN SOCIETY OF MECHANICAL ENGINEERS in 1990, a finite element model is disclosed that correlates the probability and intensity of high frequency signal transients to the development of tool wear. Similar approaches are disclosed in "An On-Line Method of Determining Tool Wear By Time Domain Analysis" authored by K. Yee and D. Blomquist, published in SOCIETY OF MANUFACTURING ENGINEERS in 1982, and "Signature Analysis Applied To Drilling" authored by S. Braun, E. Lenz and C.L. Wu, published in AMERICAN SOCIETY OF MECHANICAL ENGINEERS in 1981.
On-line wear monitoring of other cutting tools, such as those of milling operations, has been accomplished in like fashion. "Mechanical Signature Analysis In Interrupted Cutting", authored by J. Rotberg, E. Lenz and S. Braun, published in ANNALS OF THE CIRP in 1987, discloses time domain analysis of vibration signals generated during the cutting process. Signal variation during interrupted cutting, as the cutting tool engages and disengages a workpiece, can be correlated to cutting tool wear.
Various on-line systems and methods are also known for detecting failure of machine tools by analyzing signals in the time domain. One such system is disclosed in "In-Process Detection Of Tool Breakage By Monitoring The Spindle Motor Current Of A Machine Tool", authored by K. Matsushima, P. Bertok and T. Sata, published in AMERICAN SOCIETY OF MECHANICAL ENGINEERS in 1982. Tool breakage events can be detected by examination of wave form variations in the current through the motor driving the spindle that houses the cutting tool.
Other systems and methods for detecting failure of machine tools analyze various forces present during the machining process. "Computer Assisted Prediction Of Drill Failure Using In-Process Measurements Of Thrust Forces", authored by A. Thangaraj and P.K. Wright, published in AMERICAN SOCIETY OF MECHANICAL ENGINEERS in 1988, discloses the use of dynamometers to measure the thrust force of a drill on a workpiece. Time domain analysis of the resultant signal can be used to correlate thrust forces to tool failure.
Similarly, the cutting force on a workpiece by a cutting tool can also be measured by dynamometers and, through time domain analysis techniques, correlated to tool failure. Tool failure detection systems of this type are disclosed in "In-Process Detection of Tool Failure in Milling Using Cutting Forces Models", authored by Y. Altintas and I. Yellowley, published in the JOURNAL OF ENGINEERING FOR INDUSTRY in 1989, "On-Line Monitoring Of Tool And Cutting Conditions In Milling", authored by J.H. Tarn and M. Tomizuka, published in the JOURNAL OF ENGINEERING FOR INDUSTRY in 1989, and "Milling Cutter Breakage Sensing", authored by J. Tlusty published in ANNALS OF CIRP, in 1988.
Additionally, as in the area of tool wear detection, time domain analysis of vibration signals generated during the cutting process has been used to detect failure of the cutting tool. "Tool Break Detection By Monitoring Ultrasonic Vibrations", authored by S. Hayashi, C. Thomas, and D. Wildes, published in ANNALS OF THE CIRP in 1988, and "On The Use Of Drill-Up For On-Line Determination Of Drill Wear", authored by K. Yee, published in SOCIETY OF MANUFACTURING ENGINEERS in 1984, disclose on-line systems and methods for determining drill wear and breakage by applying time domain analysis to vibration or acoustic signals generated during the drilling process.
Similarly, U.S. Pat. Nos. 4,636,779 and 4,849,741, both issued to Thomas, and U.S. Pat. No. 4,642,617, issued to Thomas et al, all disclose on-line systems and methods for detecting tool breakage by applying various time domain analysis techniques to vibration signals generated during the machining process. The signal processing includes analysis techniques such as amplification, filtering and digitizing. The systems and methods can trigger tool breakage alarms based on constant values pre-set during a normal machining operation.
Each of the above methods and systems for detecting tool failure, however, suffer from a variety of problems. Since only a small number of lower quality discriminants, or criteria, are examined to determine whether the machine tool has failed, false tool failure alarms are not uncommon. Moreover, various anomalies can affect the vibration signal and its analysis in the time domain. Such anomalies can, once again, result in false triggering of tool failure alarms.
Frequency domain analysis of vibration signals generated during the cutting process has also been used to investigate failure of the cutting tool. "Linear Discriminant Function Analysis Of Acoustic Emission Signals For Cutting Tool Monitoring", authored by E. Kannatey-Asibu and E. Emel, published in MECHANICAL SYSTEMS AND SIGNAL PROCESSING in 1987, and "Statistical Process Control Of Acoustic Emission For Cutting Tool Monitoring", authored by A. Houshmand and E. Kannatey-Asibu, published in MECHANICAL SYSTEMS AND SIGNAL PROCESSING in 1989, disclose such methods. The use of frequency domain analysis lessens the effect of anomalies in the vibration signal generated during the machining process, thereby reducing the possibility of inaccurate decisions.
These methods, however, also suffer from various problems. First, only a limited number of discriminants are used in determining tool failure. As with time domain analysis tool failure detection methods, this can lead to the indication of false tool failures. Second, only pre-set threshold values for these discriminants are used. Such values are determined by machine cycles where breakage is intentionally induced. As result, the method is ill-suited for production plant applications since presetting through intentionally induced breakage is unfeasible. Finally, the method does not provide for on-line indications of tool failure.